3 hours ago
Base Salary
$405k - $485k/yr
Responsibilities
- Shape and ship the Claude Science roadmap and define technical direction for a new product category.
- Interface directly with academic labs, industry R&D teams, and research institutes to translate scientist needs into engineering priorities.
- Partner with product and design to turn scientific workflows from hypothesis through publication into shipped product experiences.
- Work with research teams to improve models for scientific applications by shaping evaluations, identifying failure modes, and feeding user feedback into model development.
- Drive cross-team alignment and take ownership of impactful product and architecture decisions.
Requirements
- 8+ years of software engineering experience.
- Ideally 2+ years at Staff or equivalent technical leadership level.
- Experience building products from 0 to 1 in fast-moving environments and setting technical direction with limited precedent.
- Experience building AI products and turning model capabilities into applications used by people.
- Ability to work directly with technical domain experts and translate their needs into priorities.
- Ability to drive cross-team alignment and influence without direct authority.
- Bachelor’s degree or equivalent combination of education, training, and/or experience in a relevant field demonstrated through coursework, training, or professional experience.
Benefits
- Annual base salary range of $405,000–$485,000 USD.
- Hybrid policy requiring staff to work from an Anthropic office at least 25% of the time, with some roles requiring more office time.
- Visa sponsorship with immigration-lawyer support where applicable.
- Competitive benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and collaborative office space.
Categories
About Anthropic
We're an AI research company that builds reliable, interpretable, and steerable AI systems. Our first product is Claude, an AI assistant for tasks at any scale. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.